Attitude Data Fusion Method, Device and System for Marine Vibroseis

Abstract

This application belongs to the technical field of vibroseis seismic exploration. Specifically, it relates to a method, device and system for attitude data fusion of marine vibroseis. By obtaining the original axis data of multiple detection sensors; fusing and filtering the roll angle data and pitch angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors; performing tilt compensation on the magnetometer data based on the roll angle data and pitch angle data of the detection sensors and calculating with the geomagnetic declination at the location of the magnetometer to obtain the tilt-compensated magnetometer data; fusing the tilt-compensated magnetometer data with the yaw angle data of the gyroscope to obtain the attitude angle data. By fusing the measurement data of multiple sensors to estimate the attitude of the vibroseis, the zero drift error of the original sensor signal is reduced, the accuracy of the attitude data is improved, and the non-offset output of the attitude information of the marine vibroseis is realized.

Description

Technical Field

[0001] The present application belongs to the technical field of controllable vibroseis seismic exploration, and specifically relates to a method, device and system for attitude data fusion of marine controllable vibroseis. Background Art

[0002] Energy security has always been the cornerstone of our country's stable development. As the cost and difficulty of land oil and gas exploitation have been rising year by year for many years, the exploration of marine oil and gas resources has become a new hotspot. Among them, the marine electromagnetic controllable vibrator is a relatively new type of controllable vibrator, which plays an important role in the field of shallow seismic exploration and is the core equipment for marine oil and gas resource detection. In actual use, how to improve the controllability of the controllable vibrator in the complex underwater environment is a difficult problem that needs to be solved urgently. The study of the multi-sensor attitude data fusion algorithm suitable for the marine electromagnetic controllable vibrator is of great significance to obtain its precise attitude.

[0003] Although high-precision attitude sensors such as fiber optic gyroscopes and mechanical electronic compasses are highly accurate, they are expensive, bulky, and difficult to operate. Therefore, in most usage scenarios, attitude sensors made using micro-electromechanical systems (MEMS) technology are a more convenient choice. A single attitude detection module is prone to offsets caused by inertial devices, so the current hot topic is to achieve accurate attitude measurement by fusing data from multiple attitude sensors. There are many types of attitude solution algorithms. Among them, the Kalman filter has been tested and found to have huge advantages in followability and smoothness over complementary filtering, making it the mainstream dynamic attitude algorithm.

[0004] The existing attitude solutions have large zero drift errors and background noise, and most of them rely on the operator's experience to judge the attitude of the source, which not only depends on manual experience, but also has a large random uncertainty. Therefore, it is imperative to study an attitude data fusion method suitable for controllable vibrators. Summary of the invention

[0005] In order to solve the problem of noise output from a single sensor and overcome the problem in related technologies that existing attitude solutions mostly rely on the operator's experience to judge the attitude of the seismic source, which not only relies heavily on manual experience but also has a large random uncertainty problem, the present application provides an attitude data fusion method, device and system for marine controllable seismic sources, which can achieve accurate, stable and drift-free output of attitude data, effectively suppress noise and improve the accuracy of attitude data.

[0006] To achieve the above objectives, this application adopts the following technical solutions:

[0007] First,

[0008] The present application provides a method for attitude data fusion of a marine controllable vibroseis, the method comprising:

[0009] Acquire the axis raw data of multiple detection sensors, the multiple detection sensors include a gyroscope, an acceleration sensor and a magnetometer, the axis raw data of the multiple detection sensors include the roll angle data and pitch angle data of the gyroscope, the yaw angle data of the gyroscope, the roll angle data and pitch angle data of the acceleration sensor, the yaw angle data of the acceleration sensor and the magnetometer data;

[0010] The roll angle data and pitch angle data of the gyroscope are integrated with the roll angle data and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors;

[0011] Performing tilt compensation on the magnetometer data based on the roll angle data and the pitch angle data of the multiple detection sensors, and adding the data to the geomagnetic declination of the location of the magnetometer to obtain the magnetometer data after tilt compensation;

[0012] The tilt-compensated magnetometer data is fused with the yaw angle data of the gyroscope to obtain attitude angle data.

[0013] Furthermore, the acquisition of the shaft raw data of the various detection sensors includes:

[0014] Step S1, when the multiple detection sensors are kept horizontally still, reading the data of the acceleration sensor and the gyroscope, selecting a suitable measurement range, dividing the axis raw data of the multiple detection sensors by the corresponding sensitivity, and calculating the zero point offset values ​​of the multiple detection sensors;

[0015] Step S2: Rotate the multiple detection sensors 360 degrees in the vertical direction and the horizontal direction respectively, read the data of the magnetometer, and calculate the zero point offset value of the magnetometer.

[0016] Furthermore, the roll angle data and pitch angle data of the gyroscope are fused and filtered with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors, including:

[0017] When the multiple detection sensors move in water, the gyroscope data and acceleration sensor data are subtracted from the zero point offsets of the multiple detection sensors, and then fusion filtering is performed to obtain the roll angle data and pitch angle data of the multiple detection sensors.

[0018] Furthermore, when the multiple detection sensors are kept horizontally still, the data of the acceleration sensor and the gyroscope are read, a suitable measurement range is selected, the axis raw data of the multiple detection sensors are divided by the corresponding sensitivity, and the zero point offset values ​​of the multiple detection sensors are obtained by solving the method, including: processing the raw data of the acceleration sensor and the raw data of the gyroscope through formula (1) and formula (2) to obtain the real data of the acceleration sensor and the real data of the gyroscope,

[0019] Raw data processing of acceleration sensor:

[0020]

[0021] Gyroscope raw data processing:

[0022]

[0023] Among them, ADCRx is the data received from the acceleration sensor, Sensitivity is the corresponding sensitivity, and ADCrate is the data received from the gyroscope.

[0024] Furthermore, when the multiple detection sensors remain horizontally stationary, data of the acceleration sensor and the gyroscope are read, a suitable measurement range is selected, and the axis raw data of the multiple detection sensors are divided by the corresponding sensitivity to calculate and obtain the zero point offset of the multiple detection sensors, including:

[0025] The method for calculating the zero point offset is: while keeping the multiple detection sensors horizontally stationary, measuring several groups of acceleration sensor data and gyroscope data, and using formula (3) to average the acceleration sensor data and gyroscope data to obtain the offset value,

[0026]

[0027] ACx1 is the first data, ...ACxn is the nth data;

[0028] The method for calculating the offset of the magnetometer is: rotate the multiple detection sensors 360 degrees in the vertical direction and the horizontal direction respectively, read the data of the magnetometer, screen out the maximum and minimum values ​​by comparison, and calculate the average value to obtain the offset value of the magnetometer. The formula is as follows:

[0029]

[0030] Max is the maximum value and Min is the minimum value.

[0031] Furthermore, after fusing and filtering the roll angle data and elevation angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and elevation angle data of multiple detection sensors, it also includes:

[0032] The roll angle data and elevation angle data of the acceleration sensor are resolved into the attitude information represented by the Euler angle, and the resolution formula is (5):

[0033]

[0034] Among them: AC x ,AC y ,AC z are the acceleration components on the x, y, and z axes respectively; ρ, φ, and γ are the pitch angle, roll angle, and yaw angle respectively.

[0035] Further, the magnetometer data is tilt-compensated based on the roll angle data of the accurate acceleration sensor and the pitch angle data of the acceleration sensor, and the magnetometer data after tilt compensation is obtained by adding the geomagnetic declination of the location of the magnetometer, including:

[0036] The tilt compensation method uses formula (6)

[0037]

[0038] Where: M x ,M y ,M z are the three-axis data output by the magnetometer; ρ, φ, γ are the acceleration

[0039] The pitch angle, roll angle, and yaw angle calculated from the sensor data; H x ,H y They are the x-axis and y-axis magnetic induction intensities after tilt compensation, which can be used to calculate the compensated yaw angle.

[0040] In a second aspect, the present application provides a device for attitude data fusion of a marine vibroseis source, the device comprising:

[0041] An acquisition module is used to acquire the axis raw data of multiple detection sensors, wherein the multiple detection sensors include a gyroscope, an acceleration sensor and a magnetometer, and the axis raw data of the multiple detection sensors include the roll angle data and pitch angle data of the gyroscope, the yaw angle data of the gyroscope, the roll angle data and pitch angle data of the acceleration sensor, the yaw angle data of the acceleration sensor and the magnetometer data;

[0042] A first fusion filtering module, which uses a fusion algorithm to fuse the roll angle data and pitch angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors;

[0043] A tilt compensation module, configured to perform tilt compensation on the magnetometer data based on the roll angle data and the pitch angle data of the multiple detection sensors, and add the data to the geomagnetic declination of the magnetometer to obtain the magnetometer data after tilt compensation;

[0044] The second fusion module is used to fuse and filter the tilt-compensated magnetometer data with the yaw angle data of the gyroscope to obtain attitude angle data.

[0045] In a third aspect, the present invention provides a system for attitude data fusion of marine controllable vibroseis, comprising:

[0046] One or more memories on which executable programs are stored;

[0047] One or more processors, used to execute the executable program in the memory to implement the steps of any method described in the first aspect.

[0048] This application adopts the above technical solution, which has at least the following beneficial effects:

[0049] The method for attitude data fusion of marine controllable seismic sources provided in the present application obtains the original axis data of multiple detection sensors, wherein the multiple detection sensors include a gyroscope, an acceleration sensor and a magnetometer, and the original axis data of the multiple detection sensors include roll angle data and pitch angle data of the gyroscope, yaw angle data of the gyroscope, roll angle data and pitch angle data of the acceleration sensor, yaw angle data of the acceleration sensor and magnetometer data; the roll angle data and pitch angle data of the gyroscope are fused and filtered with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of the multiple detection sensors; the magnetometer data is tilt-compensated based on the roll angle data and pitch angle data of the multiple detection sensors, and is added to the geomagnetic declination of the location of the magnetometer to obtain the magnetometer data after tilt compensation; the magnetometer data after tilt compensation is fused and filtered with the yaw angle data of the gyroscope to obtain attitude angle data. By fusing the measurement data of multiple sensors to estimate the attitude of the controllable source, the noise can be effectively suppressed and the accuracy of the attitude data can be improved. The data of the three sensors are filtered through a double-order Kalman fusion filter to achieve accurate, stable, and drift-free output of the attitude data. By collecting a certain amount of data, the offset is finally calculated and subtracted in subsequent calculations to achieve offset-free output.

[0050] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 is a flow chart of a method for attitude data fusion of a marine vibroseis according to an exemplary embodiment;

[0053] Figure 2 It is a schematic diagram of a Kalman filter flow chart of a posture fusion algorithm for a controllable vibrator according to an exemplary embodiment.

[0054] Figure 3 It is a yaw angle comparison diagram of an attitude fusion algorithm for a controllable vibrator according to an exemplary embodiment.

[0055] Figure 4 is a comparison diagram of a roll angle and a pitch angle of an attitude fusion algorithm for a controllable vibrator according to an exemplary embodiment.

[0056] Figure 5 It is a block diagram schematic diagram of a device for attitude data fusion of a marine vibroseis according to an exemplary embodiment;

[0057] Figure 6 It is a schematic diagram of the block diagram of a system for attitude data fusion of marine controllable vibroseis according to an exemplary embodiment.

[0058] Figure 7 It is a block diagram of a system for attitude data fusion of marine controllable seismic sources according to another exemplary embodiment.

[0059] Figure 8 It is a block diagram of a system for attitude data fusion of marine controllable seismic sources according to another exemplary embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other implementation methods obtained by ordinary technicians in the field without creative work belong to the scope of protection of the present application.

[0061] See also Figure 1 , Figure 1 is a flow chart of a method for attitude data fusion of a marine vibroseis according to an exemplary embodiment. Figure 1 As shown, the attitude data fusion method for controllable vibrator includes the following steps:

[0062] Step S101, acquiring axis raw data of multiple detection sensors, wherein the multiple detection sensors include a gyroscope, an acceleration sensor, and a magnetometer, and the axis raw data of the multiple detection sensors include roll angle data and pitch angle data of the gyroscope, yaw angle data of the gyroscope, roll angle data and pitch angle data of the acceleration sensor, yaw angle data of the acceleration sensor, and magnetometer data;

[0063] The various detection sensors described in this application are MPU9250 modules, which integrate three-axis gyroscopes, three-axis acceleration sensors, and three-axis magnetometers. This digital nine-axis IMU is an integrated nine-axis motion processing component based on I2C communication; compared with the discrete sensor solution, this solution avoids the problem of inter-axis difference between sensors, reduces a lot of packaging space, reduces system power consumption, and leaves an I2C port to bridge other sensors. Among them, the angular velocity measurement range of the gyroscope can reach up to ±2000° / s, the measurement range of the acceleration sensor is up to ±16g, and the measurement range of the magnetometer is ±4800μT.

[0064] Step S102, fusing and filtering the roll angle data and pitch angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors;

[0065] Step S103, performing tilt compensation on the magnetometer data based on the roll angle data and the pitch angle data of the multiple detection sensors, and adding the data to the geomagnetic declination of the location of the magnetometer to obtain the magnetometer data after tilt compensation;

[0066] Step S104: fusing and filtering the tilt-compensated magnetometer data and the yaw angle data of the gyroscope to obtain attitude angle data.

[0067] It should be noted that the advantages of integrated multi-detection sensors are small size, low power consumption, and high accuracy in a short time, but the accuracy will gradually decrease during long-term measurement. The drift of the gyroscope continues to increase over time, mainly due to two factors: one is the instability of the offset, that is, the initial zero point will drift linearly over time; the other is the random walk of the angle, that is, the high-frequency noise variable. Compared with the gyroscope, the accelerometer has the characteristics of long-term stability, but due to the influence of gravity, it cannot distinguish between gravity acceleration and linear acceleration, and there will be noise in a short time. The magnetometer is stable in the absence of magnetic interference and is used to measure the data of the geomagnetic field, but in the actual measurement process, many objects will generate magnetic fields similar to the geomagnetic field. The fundamental reason for whether the data drifts over time here is that the gyroscope is a relative measurement tool, and each data output is the change from the previous posture; while the accelerometer and gyroscope are absolute measurement tools, and each data output is a measurement of the current posture. Through the attitude data fusion method of the present invention, the gyroscope data and acceleration data are fused, and the first Kalman filter is performed to compensate each other to eliminate the measurement error, and accurate roll angle and pitch angle can be output; the gyroscope data and magnetometer data are fused, and the second Kalman filter is performed to compensate for the huge offset of the gyroscope yaw angle, and relatively accurate yaw angle data is obtained. Through double-order Kalman filtering, accurate attitude data is finally obtained, thereby realizing accurate measurement of the attitude of the controllable seismic source. The present invention is suitable for a controllable seismic source using an electromagnetic drive mode, and the attitude data within the effective travel of the electromagnetic controllable seismic source can be conveniently and quickly measured without disassembling the electromagnetic controllable seismic source.

[0068] It can be understood that the present application provides a method for attitude data fusion of marine controllable seismic sources, by acquiring the axis raw data of multiple detection sensors, the multiple detection sensors include gyroscopes, acceleration sensors and magnetometers, the axis raw data of the multiple detection sensors include roll angle data and pitch angle data of the gyroscope, yaw angle data of the gyroscope, roll angle data and pitch angle data of the acceleration sensor, yaw angle data of the acceleration sensor and magnetometer data; the roll angle data and pitch angle data of the gyroscope are fused and filtered with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of the multiple detection sensors; the magnetometer data is tilt compensated based on the roll angle data and pitch angle data of the multiple detection sensors, and is added to the geomagnetic declination of the location of the magnetometer to obtain the tilt-compensated magnetometer data; the tilt-compensated magnetometer data is fused and filtered with the yaw angle data of the gyroscope to obtain attitude angle data. By fusing the measurement data of multiple sensors to estimate the attitude of the controllable source, the noise can be effectively suppressed and the accuracy of the attitude data can be improved. The data of the three sensors are filtered through a double-order Kalman fusion filter to achieve accurate, stable, and drift-free output of the attitude data. By collecting a certain amount of data, the offset is finally calculated and subtracted in subsequent calculations to achieve offset-free output.

[0069] In one embodiment, the acquiring of the axis raw data of the multiple detection sensors, the multiple detection sensors including a gyroscope, an acceleration sensor and a magnetometer, the axis raw data of the multiple detection sensors including the roll angle data and pitch angle data of the gyroscope, the yaw angle data of the gyroscope, the roll angle data and pitch angle data of the acceleration sensor, the yaw angle data of the acceleration sensor and the magnetometer data, includes:

[0070] Step S1, when the multiple detection sensors are kept horizontally still, reading the data of the acceleration sensor and the gyroscope, selecting a suitable measurement range, dividing the axis raw data of the multiple detection sensors by the corresponding sensitivity, and calculating the zero point offset values ​​of the multiple detection sensors;

[0071] Step S2: Rotate the multiple detection sensors 360 degrees in the vertical direction and the horizontal direction respectively, read the data of the magnetometer, and calculate the zero point offset value of the magnetometer.

[0072] In the field of posture measurement, each sensor has a zero point offset due to the inherent characteristics of the core sensitive device of the sensor, resulting in an indefinite amount of error in the measured data. In the present invention, for each sensor, by collecting a certain amount of data, an offset value is finally calculated, and the offset value is subtracted in subsequent calculations to achieve an output without offset.

[0073] In one embodiment, the roll angle data and pitch angle data of the gyroscope are fused and filtered with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors, including:

[0074] When the multiple detection sensors move in water, the gyroscope data and acceleration sensor data are subtracted from the zero point offsets of the multiple detection sensors, and then fusion filtering is performed to obtain the roll angle data and pitch angle data of the multiple detection sensors.

[0075] The flowchart of the Kalman filter algorithm is as follows Figure 2 It can be seen that Kalman filtering is an optimal data processing method that uses the observed quantity of the system as the input of the filter and the estimated value of the system state quantity as the output of the filter. Assume that the state equation and measurement variance of the system are as shown in formula (9), which is used to describe the evolution process of the system state after prediction and update.

[0076]

[0077] Where: x k is the current state vector, x k-1 is the state vector of the previous moment, A is the state transfer matrix from moment k-1 to moment k, U k is the system input control vector, B is the control input matrix, w k is the process noise; y k is the current observation vector, H is the gain matrix from the current state to the observation, v k is the observation noise.

[0078] Kalman filtering is a recursive estimation method, which consists of two parts: prediction and update. In the prediction phase, the filter uses the data of the state at the previous moment to make an estimate of the current state. In the update phase, the filter uses the observed value of the current state to optimize the predicted value of the prediction phase. The following five core formulas are used to describe the specific steps of Kalman filtering:

[0079] The estimated state quantity,

[0080]

[0081] Error covariance pre-estimation,

[0082] P k|k-1 =AP k-1 A T +Q (11)

[0083] Kalman gain update,

[0084] K k =P k|k-1 H T[HP k|k-1 H T +R] -1 (12)

[0085] The current state estimate is updated,

[0086]

[0087] Error covariance update,

[0088] P k =(IK k H)P k|k-1 (14)

[0089] in: is the current state vector; is the estimate of the current state vector based on the state vector at the previous moment; A is the state transfer matrix from moment k-1 to moment k; U k is the system input control vector; B is the control input matrix; y k is the current observation vector; H is the gain matrix from the current state quantity to the observation quantity; K k For the observation error The modified weight of P k is the error covariance matrix of the current state estimate; P k|-1 is the estimate of the current error covariance matrix based on the error covariance matrix at the previous moment; I is the unit matrix.

[0090] This application performs first-order fusion filtering on the roll angle and pitch angle data of the gyroscope and acceleration sensor, and then performs second-order fusion filtering on the yaw angle data of the magnetometer and gyroscope after tilt compensation to obtain the final attitude angle, which effectively suppresses noise and improves the accuracy of attitude data.

[0091] In one embodiment, when the multiple detection sensors remain horizontally stationary, the data of the acceleration sensor and the gyroscope are read, a suitable measurement range is selected, the axis raw data of the multiple detection sensors are divided by the corresponding sensitivity, and the zero point offset values ​​of the multiple detection sensors are obtained by solving the method, including: processing the raw data of the acceleration sensor and the raw data of the gyroscope through formula (1) and formula (2) to obtain the real data of the acceleration sensor and the real data of the gyroscope,

[0092] The raw data processing formula of the acceleration sensor is:

[0093] The raw data processing formula of the gyroscope is:

[0094]

[0095] Among them, ADCRx is the data received from the acceleration sensor, Sensitivity is the corresponding sensitivity, and ADCrate is the data received from the gyroscope.

[0096] In one embodiment, when the multiple detection sensors remain horizontally stationary, data of the acceleration sensor and the gyroscope are read, a suitable measurement range is selected, and the axis raw data of the multiple detection sensors are divided by the corresponding sensitivity to calculate the zero point offset of the multiple detection sensors, including:

[0097] The method for calculating the zero point offset is: while keeping the multiple detection sensors horizontally stationary, measuring several groups of acceleration sensor data and gyroscope data, and using formula (3) to average the acceleration sensor data and gyroscope data to obtain the offset value,

[0098]

[0099] Among them, ACx1 is the first data...ACxn is the nth data;

[0100] The method for calculating the offset of the magnetometer is: rotate the multiple detection sensors 360 degrees in the vertical direction and the horizontal direction respectively, read the data of the magnetometer, screen out the maximum and minimum values ​​by comparison, and calculate the average value to obtain the offset value of the magnetometer. The formula is as follows:

[0101]

[0102] Among them, Max is the maximum value and Min is the minimum value.

[0103] In one embodiment, after fusing and filtering the roll angle data and pitch angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and elevation angle data of multiple detection sensors, the method further includes:

[0104] The roll angle data and elevation angle data of the acceleration sensor are resolved into the attitude information represented by the Euler angle, and the resolution formula is (5):

[0105]

[0106] Among them: AC x ,AC y ,AC z are the acceleration components on the x, y, and z axes respectively; ρ, φ, and γ are the pitch angle, roll angle, and yaw angle respectively.

[0107] The information sent back by the sensor represents the measured acceleration, angular velocity and magnetic induction intensity, which need to be solved into attitude information represented by Euler angles. The acceleration sensor measures the acceleration components of the three axes, and the three Euler angles representing the attitude can be calculated using formula (5).

[0108] The gyroscope measures the angular velocity components of the three axes, and the corresponding angle value can be obtained by integration. The calculation formula is as follows:

[0109]

[0110] Where: θ k is the angle value of the current state; θ k-1 is the angle value at the previous moment; ω k The angular velocity of the current state measured by the gyroscope; is the offset of the angular velocity at the current moment; dt is the integration time, which is determined by the sampling frequency of the sensor.

[0111] The magnetometer measures the magnetic induction intensity of three axes. When the magnetometer is in a horizontal position, the yaw angle can be calculated by the following formula (7):

[0112]

[0113] Among them, H x ,H y They are the magnetic induction intensity output on the x and y axes respectively.

[0114] However, in actual use, the magnetometer cannot be accurately in a horizontal position, and the data of the acceleration sensor needs to be used for tilt compensation to reduce the error of the magnetometer heading angle calculation. The formula for tilt compensation is as follows:

[0115]

[0116] Where: M x ,M y ,M z are the three-axis data output by the magnetometer; ρ, φ, γ are the acceleration

[0117] The pitch angle, roll angle, and yaw angle are calculated from the sensor data. x ,H y They are the x-axis and y-axis magnetic induction intensities after tilt compensation, which can be used to calculate the compensated yaw angle.

[0118] To verify the effectiveness of the algorithm, the sensor was picked up to simulate the posture changes in motion, and nine-axis data was collected at the same time, and the original data was compared with the data calculated by the algorithm. Figure 3 and Figure 4As shown in the figure, before the fusion filter, each curve has noise of varying sizes, but the fusion filter suppresses the noise very well and outputs a smooth curve. For the yaw angle data, before the fusion filter, the gyroscope data is constantly drifting due to integration and accumulation, but the fusion filter adds the magnetometer data, so that the yaw angle no longer drifts and achieves good accuracy.

[0119] See also Figure 5 , Figure 5 1 is a schematic diagram of a device structure for attitude data fusion of a marine vibrator in one embodiment of the present application. The device 5 for attitude data fusion of a marine vibrator includes:

[0120] An acquisition module 501 is used to acquire axis raw data of multiple detection sensors, wherein the multiple detection sensors include a gyroscope, an acceleration sensor, and a magnetometer, and the axis raw data of the multiple detection sensors include roll angle data and pitch angle data of the gyroscope, yaw angle data of the gyroscope, roll angle data and pitch angle data of the acceleration sensor, yaw angle data of the acceleration sensor, and magnetometer data;

[0121] A first fusion filtering module 502 is used to fuse and filter the roll angle data and pitch angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors;

[0122] A tilt compensation module 503, configured to perform tilt compensation on the magnetometer data based on the roll angle data and the pitch angle data of the multiple detection sensors, and add the data to the geomagnetic declination of the magnetometer location to obtain the magnetometer data after tilt compensation;

[0123] The second fusion filtering module 504 is used to fuse and filter the tilt-compensated magnetometer data with the yaw angle data of the gyroscope to obtain attitude angle data.

[0124] Regarding the device 5 for attitude data fusion of controllable vibrator in the above embodiment, the specific manner in which each module performs operations has been described in detail in the above embodiment of the related method, and will not be elaborated here.

[0125] See also Figure 6 , Figure 6 is a block diagram of a posture data fusion system for marine vibroseis according to an exemplary embodiment. Figure 6 As shown, the attitude data fusion system 6 for controllable vibrator includes:

[0126] One or more memories 601 on which executable programs are stored;

[0127] One or more processors 602 are used to execute the executable program in the memory 301 to implement the steps of any of the above methods.

[0128] As a further improvement of the above embodiment, in one embodiment, a system for attitude data fusion of marine controllable seismic sources also includes one or more acceleration sensors, one or more magnetometers, one or more gyroscopes, one or more UART-to-USB converters, one or more displays, and one or more GPS modules.

[0129] See also Figure 7 , Figure 7 It is a block diagram schematic diagram of a posture data fusion system for marine controllable seismic sources shown in another embodiment of the present application.

[0130] See also Figure 8 , Figure 8 It is a block diagram schematic diagram of a posture data fusion system for marine controllable seismic sources shown in another embodiment of the present application.

[0131] It can be understood that, through the attitude data fusion system for controllable seismic sources provided by the present invention, the gyroscope data and acceleration data are fused, and the first fusion filtering is performed to compensate each other to eliminate the measurement error, and accurate roll angle and pitch angle can be output; the gyroscope data and magnetometer data are fused, and the second fusion filtering is performed to compensate for the huge offset of the yaw angle of the gyroscope, and relatively accurate yaw angle data is obtained. Through double-order fusion filtering, accurate attitude data is finally obtained, thereby realizing accurate measurement of the attitude of the controllable seismic source. The present invention is suitable for controllable seismic sources using electromagnetic drive, and the attitude data within the effective travel range of the electromagnetic controllable seismic source can be conveniently and quickly measured without disassembling the electromagnetic controllable seismic source.

[0132] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0133] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" and "multiple" refers to at least two.

[0134] It should be understood that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or there may be an intermediate element at the same time; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intermediate element at the same time. In addition, the "connection" used here may include wireless connection; the wording "and / or" used includes any unit and all combinations of one or more associated listed items.

[0135] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0136] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0137] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0138] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0139] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0140] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0141] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for attitude data fusion of marine vibroseis, characterized in that, the method includes: Obtaining the axis raw data of multiple detection sensors, the multiple detection sensors include gyroscopes, acceleration sensors and magnetometers, and the axis raw data of the multiple detection sensors include the roll angle data and pitch angle data of the gyroscope, the yaw angle data of the gyroscope, the roll angle data and pitch angle data of the acceleration sensor, the yaw angle data of the acceleration sensor, and magnetometer data; Fusing and filtering the roll angle data and elevation angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors; Performing tilt compensation on the magnetometer data based on the roll angle data and pitch angle data of the multiple detection sensors, and adding the geomagnetic declination at the location of the magnetometer to obtain the magnetometer data after tilt compensation; Fusing and filtering the magnetometer data after tilt compensation with the yaw angle data of the gyroscope to obtain attitude angle data.

2. The method according to claim 1, characterized in that, the obtaining of the axis raw data of multiple detection sensors includes: Step S1, when the multiple detection sensors are kept horizontally stationary, reading the data of the acceleration sensor and the gyroscope, selecting a suitable measurement range, dividing the axis raw data of the multiple detection sensors by the corresponding sensitivity, and resolving to obtain the zero offset values of the multiple detection sensors; Step S2, rotating the multiple detection sensors 360 degrees each along the vertical and horizontal directions, reading the data of the magnetometer, and resolving to obtain the zero offset value of the magnetometer.

3. The method according to claim 1, characterized in that, the fusing and filtering of the roll angle data and pitch angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of multiple detection sensors includes: When the multiple detection sensors are moving in water, subtracting the zero offsets of the multiple detection sensors from the gyroscope data and acceleration sensor data, and then performing fusion filtering to obtain the roll angle data and pitch angle data of the multiple detection sensors.

4. The method according to claim 2, characterized in that, the reading of the data of the acceleration sensor and the gyroscope when the multiple detection sensors are kept horizontally stationary, selecting a suitable measurement range, dividing the axis raw data of the multiple detection sensors by the corresponding sensitivity, and resolving to obtain the zero offset values of the multiple detection sensors includes: Processing the raw data of the acceleration sensor and the raw data of the gyroscope through formula (1) and formula (2) to obtain the real data of the acceleration sensor and the real data of the gyroscope, Processing of the raw data of the acceleration sensor: Processing of the raw data of the gyroscope: wherein, ADCRx is the data of the received acceleration sensor, Sensitivity is the corresponding sensitivity, and ADCrate is the data of the received gyroscope.

5. The method according to claim 2 or 4, characterized in that, When the multiple detection sensors are kept horizontally stationary, read the data of the acceleration sensor and the gyroscope, select an appropriate measurement range, and divide the original axis data of the multiple detection sensors by the corresponding sensitivities to calculate the zero offsets of the multiple detection sensors, including: The method for calculating the zero offset is as follows: When the multiple detection sensors are kept horizontally stationary, measure several groups of data of the acceleration sensor and the gyroscope, and use formula (3) to obtain the offset values of the data of the acceleration sensor and the gyroscope by averaging. Where ACx1 is the first data,... ACxn is the nth data; The method for calculating the offset of the magnetometer is: Rotate the multiple detection sensors 360 degrees each along the vertical and horizontal directions, read the data of the magnetometer, and through comparison, screen out the maximum value and the minimum value, and calculate the average value to obtain the offset value of the magnetometer. The formula is as follows: Where Max is the maximum value and Min is the minimum value.

6. The method according to claim 1, characterized in that, When fusing and filtering the roll angle data and pitch angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of the multiple detection sensors, it further includes: Resolve the roll angle data and elevation angle data of the acceleration sensor into attitude information represented by Euler angles. The resolution formula is (5): Where: AC x , AC y , AC z are the acceleration components on the x, y, and z axes respectively; ρ, φ, and γ are the pitch angle, roll angle, and yaw angle respectively.

7. The method according to claim 1, characterized in that, Based on the roll angle data and pitch angle data of the multiple detection sensors, perform tilt compensation on the magnetometer data, and add it to the geomagnetic declination of the location where the magnetometer is located to obtain the tilt-compensated magnetometer data, including: The tilt compensation method uses formula (6) Where: M x , M y , M z are the three-axis data output by the magnetometer respectively; ρ, φ, γ are the pitch angle, roll angle, and yaw angle calculated using the acceleration sensor data respectively; H′ x , H′ y are the magnetic induction intensities of the x and y axes after inclination compensation respectively, which can be used to calculate the yaw angle after compensation.

8. An apparatus for attitude data fusion of an ocean vibrator, characterized in that, The apparatus includes: An acquisition module, configured to acquire the original axis data of multiple detection sensors. The multiple detection sensors include a gyroscope, an acceleration sensor, and a magnetometer. The original axis data of the multiple detection sensors includes the roll angle data and pitch angle data of the gyroscope, the yaw angle data of the gyroscope, the roll angle data and pitch angle data of the acceleration sensor, the yaw angle data of the acceleration sensor, and the magnetometer data; A first fusion filtering module, configured to fuse and filter the roll angle data and pitch angle data of the gyroscope with the roll angle and pitch angle data of the acceleration sensor to obtain the roll angle data and pitch angle data of the multiple detection sensors; A tilt compensation module, configured to perform tilt compensation on the magnetometer data based on the roll angle data and pitch angle data of the multiple detection sensors, and add it to the geomagnetic declination of the location where the magnetometer is located to obtain the tilt-compensated magnetometer data; A second fusion filtering module, configured to fuse and filter the tilt-compensated magnetometer data with the yaw angle data of the gyroscope to obtain attitude angle data.

9. A system for attitude data fusion of an ocean vibrator, characterized in that, includes: One or more memories, on which executable programs are stored; One or more processors for executing the executable program in the memory to implement the steps of the method according to any one of claims 1-7.

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